>> Voiceover: Live from Las Vegas, it's theCUBE covering Dell EMC World 2017. Brought to you by Dell EMC. >> Welcome back to theCUBE's coverage of Dell EMC World. I'm your host, Rebecca Knight, along with my co-host, Keith Townsend. We are joined by Kevin Roche. He is the president of Dell EMC consulting services. >> Thank you. Nice to be here. Thank you. >> It's great, you're a veteran, so we're happy to have you. Talk a little bit about what's new with the services consulting business, particularly since the merger. >> If you don't mind, I'll put some context around this. As we brought Dell and EMC together, the services piece became a really critical part for our customers' journey as you think about the transformation they're going through, the consumption of our technology. We have right now, today, Dell EMC services is about 30 thousand people globally today. We're augmented with another 30 thousand partners across the globe. We're able to deliver services directly in about 160 countries and our ability to look at the entire portfolio from consulting to design, to deployment of technology, and then the ongoing support of that and then the managed services piece of that. We feel we're in an interesting position to be able to help our customers through any parts of the transformation they're going through and those conversations have been really relevant to them. I think they've really appreciated that. A coming together of Dell, EMC and the role services plays has really been fascinating. We're excited about that role. You heard it earlier today with Michael and David Goulden at the keynotes, they really emphasized the importance of services and bringing this technology and helping our customers through their transformations. We're excited about playing that role and helping them through that process. >> What's on your customers' mind as they begin to realize this digital transformation? What are you hearing from them? >> It's interesting. I've been fortunate this morning to have had three customer meetings today. Last night we had a great customer appreciation event. The one thing that I'm finding, if we go out and say, "Here's the story," I don't think we are doing the right service. I think what you really have to get to is that you have to first listen to customers because they're at various stages of that journey. Where they are in their digital transformation. Some more mature companies are further along, others are just starting this journey. If we come out and say, "Here's the cloud story," you could offend people who are further along in their journey or you could just be talking over people who haven't started. I think the most important thing that we're finding is making sure that we're listening to our customers. It's the most important thing we can do within the services team. A big part of that is listening to where they are and what we're finding is, we just did a study, and we found that of the companies who identified themselves, less than 5% are identifying themselves as mature in that digital transformation. Which means that there's still a long way through a process that they have to get through on their digital transformation. We're pretty excited about the role that we can play in helping them with that. We feel right now what we're hearing from customers is, the opportunity is there to help them. They are looking for answers. They are looking for best practices. They are looking for, What are others in our industry doing so that I can help apply that to my executive team to convince them to take the journey, to make the investment of the transformation we have to go through. It's really kind of fun to listen to them. It's exciting that they want this right now. You know that they have to do something. It's exciting that we can play a role in that. >> Kevin, let's talk a little bit about the transformation inside of Dell EMC. EMC was a very respected, but very big, professional services even before the merger, with a lot of products, the cover and the storage, hybrid cloud, just a lot of services. Combine that with Dell. The workforce has to be overwhelmed by the sheer depth of services offered by the new Dell EMC. How has that transformation occurred and how's it going inside? >> I appreciate you asking, because I think that's one of the things we've been able to share with our customers. Is the transformation we just went through. From the combination of Dell and EMC coming together. You're right, there's a little bit of history from both companies. One of the things that we've wanted to do is preserve the value that we've created to those customers in the different segments, where Dell has been great on the server and client side of things. You can't lose that intimacy and the relationship that they had with those customers. It's really an important piece. You certainly couldn't change the model that EMC has traditionally had, of the large enterprise customers. We wanted to make sure that intimacy and that relationship with those customers are really important. So we've been blending the best-of as we brought the teams together. If you look at the leadership team and the people we've put in leadership roles, and as we looked at bringing the portfolio together, it's been a bit of best-of-breed, if you will as we've landed now the Dell EMC team. Our team members across the globe, there has been a little bit of now assuring of, What does this mean? and the complexity of this large transformation. We've asked them to do one thing for us. Focus on delivering services to our customers. The rest will fall into place. Just stay focused on our customers and all of the indications seem like we're moving in the right direction and our team members across the globe have certainly done that. I think that was a big guiding principle to ask our team members, "Stay focused. Don't let the noise distract you from the execution and the service we have to have with our customers." >> A lot of weapons, a lot of tools in the toolbelt before pulse merger. What are the folks in the field, the consultants, what are they most excited about when they are talking about visible transformation? >> There's a couple of things that we're really excited about part of this. The first starts with the market. We talked a little bit earlier, customers in the market are asking for help in their transformation. It's no longer, "Hey we think you should transform." Now it's customers in the market asking for help of some sort. We're excited about that. The first thing is, when the market drags you into things, you get excited about that, as opposed to trying to push something. The second, most of our consulting team are pretty excited with now having the identity of being the place to go to help in the transformation. We think that that's a very important role that we're going to play. We're aligned very well with our core sales teams. We're going in now with one campaign that's consistent across the whole company. IT transformation, digital transformation, workforce transformation and security transformation. That not only talks about it from a product portfolio, but all the way through in the consulting side. We know that there's a role that we can play to help our customers. They were actually pretty excited about the alignment piece, and the demand that's coming from the marketplace in this. >> Can you talk a little bit about the learning that you're discovering and you mentioned best practices. What has emerged and is there a template for, This is how it happens in healthcare. This is how it goes in financial services. >> It's interesting that you bring that up, actually. Some of the customers earlier today, one in healthcare, a couple in financial institutions. They were asking that question a little bit. Without sharing obviously the confidentiality of our customers, we are able to aggregate what's happening in those industries. Those two industries in particular, especially the more proven companies, their ability to move quickly through that transformation is probably going to take a little bit longer. They have to deal with confidentiality and financial and legal requirements in their industry that they have to make sure they preserve that value, that trust that they have with their customers. For them to move too quickly through that transformation could jeopardize their value proposition in the marketplace. I think you're seeing those industries and some of the traditional customers taking an appropriate pace. That pace and sharing of best practices could start with modernization of the infrastructure. It could start with, I want to change my operating model within how I operate IT. It could be my endpoint. I want to make them more trusted, more valuable, et cetera. It could start the transformation from a workforce standpoint. We're pretty excited about where that's going and the ability to share that with our customers. I think what we're seeing is not one template is perfect, but when you think about the transformation in three areas: IT infrastructure, the operating model of IT and then what you want to do with your applications, I think those things become really critical. You think about the projects that are probably pretty consistent across every single industry today. >> You mentioned that most companies really get this. That they are on board. But there have to be some that are resisting. What's your advice for those, even on a workforce level, that are being curmudgeonly or resisting because it's new? >> It's a great question. We would be saying, "A successful project looks like this." First you have to have executive-level sponsorship. You need somebody at the executive level in the company, whether that's the CFO, CIO, somebody who takes sponsorship for this transformation. That basically can shepherd through bouncy times through that. One of the things that we would say for a company who might be a little bit reluctant, Who is that sponsor? Who is that person who is going to help shepherd the transformational message at the executive level with the company? The second piece: allow participation of the business. It's not an IT project anymore. This is a project that's a shared project. It's the business and IT coming together and being able to share that experience and trade-offs of priorities of what's important and not. Build the project governance in place to allow you to be successful in these projects. We think those things, helping some of those customers that might be a little bit reluctant, those are the things you need to put in place. Then what we've been suggesting for some companies who might be a little bit reluctant, take a piece of this. Find a pilot. >> Experiment. >> Find a proof point. Give yourself a chance to validate some of your assumptions, some of your guiding principles, to be able to demonstrate to your executive sponsor, If we make these changes, this is the value that you can get. Those, I call them assessments or pilots or small-sample areas, have seemed to resonate really well with some of those customers that might be a little bit reluctant to begin that journey. >> Can we talk about the inverse? The customers that might be a little bit too excited. DevOps the world and what's that conversation? That education conversation. What has that been like? >> That's actually probably the harder one, right? Where they probably want to go at a pace that might be a little bit challenging and risky for them. How do we make sure that that risk has been balanced appropriate? What we want to do is again, share some of those best practices in those environments and say, Here's three other companies in your industry. Here's how fast they went. Here's what they have tackled. We never want to slow down a customer. If they want to take the risk, our job is to make sure we expose the risk so that they can make the best decisions. This has to be a customer decision. Our job, I think, is advise and present the risk and the opportunity associated with the speed or the pace that they want to go. I think that's the most important thing that we can do. >> Kevin, thanks so much. This has been great. >> It's been fun. Thank you very much for the time. We really appreciate it. We're pretty excited about the role we can play within Dell EMC services to help our customers through this transformation. Enjoy the rest of the week. >> Thanks, you too. >> Thank you. >> I'm Rebecca Knight, for my host Keith Townsend. We'll be back with more at Dell EMC World after this. (techno music)

>>Hey, welcome back everyone to the cubes coverage of eaters reinvent 2021. So our third day wall-to-wall coverage. I'm my coach, Dave Alonzo. He we're getting all the action two sets in person. It's also a virtual hybrid events with a lot of great content online, bringing you all the fresh voices, all the knowledge, all the news and all the action and got great guests here today. As your renderer, managing director of AWS is Europe, middle east, and Africa also known as EMIA. Welcome to the cube. Welcome, >>Welcome. Thanks for coming on. Lovely to be here. >>So Europe is really hot. Middle east Africa. Great growth. The VC culture in Europe specifically has been booming this year. A lot of great action. We've done many cube gigs out there talking to folks, uh, entrepreneurship, cloud, native growth, and then for us it's global. It's awesome. So first question got to ask you is, is you're new to AWS? What brought you here? >>Yeah, no, John, thank you so much. I've been here about three and a half months now, actually. Um, so what brought me here? Um, I have been in and around the tech world since I was a baby. Um, my father was an entrepreneur. I sold fax machines and microfilm equipment in my early days. And then my career has spanned technology in some form or the other. I was at EMC when we bought VMware. Uh, I was a Colt when we did a FinTech startup joined Schneider in my background, which is industrial tech. So I guess I'm a bit of a tech nerd, although I'm not an engineer, that's for sure. The other thing is I've spent a huge part of my career advising clients. And so while I was at McKinsey on business transformation and cloud keeps coming up, especially post pandemic, huge, huge, huge enabler, right of transformation. So when I got the call from AWS, I thought here's my opportunity to finally take what companies are wrestling with, bring together a pioneer in cloud with our enterprise and start-up and SMB clients connect those dots between business and technology and make things happen. So it real magic. So that's what brought me here. And I guess the only other thing to say is I'd heard a lot of other culture, customer mash, obsession, and leadership principles. >>That's why I'm here. It's been a great success. I got to ask you too, now that your new ostium McKinsey, even seeing the front lines, all the transformation, the pandemic has really forced everybody globally to move faster. Uh, things like connect were popular in EMEA. How, how is that going out? There's at the same kind of global pressure on the digital transformation with cloud? What are you seeing out there? >>I've been traveling since I joined, uh, around 10 of the countries already. So Ben planes, trains, automobiles, and what you definitely see is massive acceleration. And I think it's around reinvention of the business. So people are adopting cloud because it's obviously there's cost reasons. There's MNA reasons. There's really increasingly more about innovating. How do I innovate my business? How do I reinvent my business? So you see that constantly. Um, and whether you're a enterprise company or you're a startup, they're all adopting cloud in different, different ways. Um, I mean, I want to tell a core to stack because it's really interesting. And Adam mentioned this in his keynote five to 15% only of workloads have moved to the cloud. So there's a tremendous runway ahead of us. Um, and the three big things on people's minds helped me become a tech company. So it doesn't matter who you are, you're retail, whether you're life sciences or healthcare. You've probably heard about the Roche, uh, work that we're doing with Roche around accelerating R and D with data, or if you're a shoes Addie desk, how do you accelerate again, your personalized experiences? So it doesn't matter who you have helped me become a tech company, give me skills, digital skills, and then help me become a more sustainable company. Those are the three big things I'm thinking of. >>So a couple of things to unpack there. So think about it. Transformation. We still have a long way to go to your point, whatever 10, 15%, depending on which numbers you look at. We've been talking a lot in the cube about the next decade around business transformation, deeper business integration, and the four smarts to digital. And the woke us up to that, accelerated that as you say, so as you travel around to customers in AMEA, what are you hearing with regard to that? I mean, many customers maybe didn't have time to plan. Now they can sit back and take what they've learned. What are you hearing? >>Yeah. And it's, it's a little bit different in different places, right? So, I mean, if you start, if you look at, uh, you know, our businesses, for example, in France, if you look at our businesses in Iberia or Italy, a lot of them are now starting they're on the, at least on the enterprise front, they are now starting to adopt cloud. So they stepping back and thinking about their overall strategy, right? And then the way that they're doing it is actually they're using data as the first trigger point. And I think that makes it easier to migrate because if you, if you look at large enterprises and if you think of the big processes that they've got and all the mainframes and everything that they need to do, if you S if you look at it as one big block, it's too difficult. But when you think about data, you can actually start to aggregate all of your data into one area and then start to analyze and unpack that. >>So I think what I'm seeing for sure is in those countries, data is the first trigger. If you go out to Israel, well that you've got all, it's really start up nation as you know, right. And then we've got more of the digital natives and they want to, you know, absorb all of the innovation that we're throwing at them. And you've heard a lot here at reinvent on some of the things, whether it's digital twins or robotics, or frankly, even using 5g private network, we've just announcement. They are adopting innovation and really taking that in. So it really does differ, but I think the one big message I would leave you with is bringing industry solutions to business is critical. So rather than just talking it and technology, we've got to be able to bring some of what we've done. So for example, the Goldman Sachs financial cloud, bring that to the rest of financial services companies and the media, or if you take the work we're doing on industrials and IOT. So it's really about connecting what industry use cases with. >>What's interesting about the Goldman Dave and I were commenting. I think we coined the term, the story we wrote on Thursday last week, and then PIP was Sunday superclouds because you look at the rise of snowflake and Databricks and Goldman Sachs. You're going to start to see people building on AWS and building these super clouds because they are taking unique platform features of AWS and then sacrificing it for their needs, and then offering that as a service. So there's kind of a whole nother tier developing in the natural evolution of clouds. So the partners are on fire right now because the creativity, the market opportunities are there to be captured. So you're seeing this opportunity recognition, opportunity, capture vibe going on. And it's interesting. I'd love to get your thoughts on how you see that, because certainly the VCs are here in force. I did when I saw all the top Silicon valley VCs here, um, and some European VCs are all here. They're all seeing this. >>So pick up on two things you mentioned that I think absolutely spot on. We're absolutely seeing with our partners, this integration on our platform is so important. So we talk about the power of three, which is you bring a JSI partner, you bring an ISV partner, you bring AWS, you create that power of three and you take it to our customers. And it doesn't matter which industry we are. Our partner ecosystem is so rich. The Adam mentioned, we have a hundred thousand partners around the world, and then you integrate that with marketplace. Um, and the AWS marketplace just opens the world. We have about 325,000 active customers on marketplace. So sassiphy cation integration with our platform, bringing in the GSI and the NSIs. I think that's the real power to, to, to coming back to your point on transformation on the second one, the unicorns, you know, it's interesting. >>So UK France, um, Israel, Mia, I spent a lot of time, uh, recently in Dubai and you can see it happening there. Uh, Africa, Nigeria, South Africa, I mean all across those countries, you're saying huge amount of VC funding going in towards developers, towards startups to at scale-ups more and more of a, um, our startup clients, by the way, uh, are actually going IPO. You know, initially it used to be a lot of M and a and strategic acquisitions, but they have actually bigger aspirations and they're going IPO and we've seen them through from when they were seed or pre-seed all the way to now that they are unicorns. Right? So that there's just a tremendous amount happening in EMEA. Um, and we're fueling that, you know, you know, I mean, born in the cloud is easy, right? In terms of what AWS brings to the table. >>Well, I've been sacred for years. I always talked to Andy Jassy about this. Cause he's a big sports nut. When you bring like these stadiums to certain cities that rejuvenates and Amazon regions are bringing local rejuvenation around the digital economies. And what you see with the startup culture is the ecosystems around it. And Silicon valley thrives because you have all the service providers, you have all the fear of failure goes away. There's support systems. You start to see now with AWS as ecosystem, that same ecosystem support the robustness of it. So, you know, it's classic, rising tide floats all boats kind of vibe. So, I mean, we don't really have our narrative get down on this, but we're seeing this ecosystem kind of play going on. Yeah. >>And actually it's a real virtuous circle, or we call flywheel right within AWS because a startup wants to connect to an enterprise. An enterprise wants to connect to a startup, right? A lot of our ISV partners, by the way, were startups. Now they've graduated and they're like very large. So what we are, I see our role. And by the way, this is one of the other reasons I came here is I see our role to be able to be real facilitators of these ecosystems. Right. And, you know, we've got something that we kicked off in EMEA, which I'm really proud of called our EMEA startup loft accelerator. And we launched that a web summit. And the idea is to bring startups into our space virtually and physically and help them build and help them make those connections. So I think really, I really do think, and I enterprise clients are asking us all the time, right? Who do I need to involve if I'm thinking IOT, who do I need to involve if I want to do something with data. And that's what we do. Super connectors, >>John, you mentioned the, the Goldman deal. And I think it was Adam in his keynote was talking about our customers are asking us to teach them how to essentially build a Supercloud. I mean, our words. But so with your McKinsey background, I would imagine there's real opportunities there, especially as you, I hear you talk about IMIA going around to see customers. There must be a lot of, sort of non-digital businesses that are now transforming to digital. A lot of capital needs there, but maybe you could talk about sort of how you see that playing out over the next several years in your role and AWS's role in affecting that transfer. >>Yeah, no, absolutely. I mean, you're right actually. And I, you know, maybe I will, from my past experience pick up on something, you know, I was in the world of industry, uh, with Schneider as an example. And, you know, we did business through the channel. Um, and a lot of our channel was not digitized. You know, you had point of sale, electrical distributors, wholesalers, et cetera. I think all of those businesses during the pandemic realized that they had to go digital and online. Right. And so they started from having one fax machine in a store. Real literally I'm not kidding nothing else to actually having to go online and be able to do click and collect and various other things. And we were able with AWS, you can spin up in minutes, right. That sort of service, right. I love the fact that you have a credit card you can get onto our cloud. >>Right. That's the whole thing. And it's about instances. John Adam talked about instances, which I think is great. How do businesses transform? And again, I think it's about unpacking the problem, right? So what we do a lot is we sit down with our customers and we actually map a migration journey with them, right? We look across their core infrastructure. We look at their SAP systems. For example, we look at what's happening in the various businesses, their e-commerce systems, that customer life cycle value management systems. I think you've got to go business by business by business use case by use case, by use case, and then help our technology enable that use case to actually digitize. And whether it's front office or back office. I think the advantages are pretty clear. It's more, I think the difficulty is not technology anymore. The difficulty is mindset, leadership, commitment, the operating model, the organizational model and skills. And so what we have to do is AWS is bringing not only our technology, but our culture of innovation and our digital innovation teams to help our clients on that journey >>Technology. Well, we really appreciate you taking the time coming on the cube. We have a couple more minutes. I do want to get into what's your agenda. Now that you're got you're in charge, got the landscape and the 20 mile stare in front of you. Cloud's booming. You got some personal passion projects. Tell us what your plans are. >>So, um, three or four things, right? Three or four, really big takeaways for me is one. I, I came here to help make sure our customers could leverage the power of the cloud. So I will not feel like my job's been done if I haven't been able to do that. So, you know, that five to 15% we talked about, we've got to go 50, 60, 70%. That that's, that's the goal, right? And why not a hundred percent at some point, right? So I think over the next few years, that's the acceleration we need to help bring in AMEA Americas already started to get there as you know, much more, and we need to drive that into me. And then eventually our APJ colleagues are going to do the same. So that's one thing. The other is we talked about partners. I really want to accelerate and expand our partner ecosystem. >>Um, we have actually a huge growth by the way, in the number of partners signing up the number of certifications they're taking, I really, really want to double down on our partners and actually do what they ask us for, which is join. Co-sell joined marketing globalization. So that's two, I think the third big thing is when you mentioned industry industry industry, we've got to bring real use cases and solutions to our customers and not only talk technology got to connect those two dots. And we have lots of examples to bring by the way. Um, and then for hire and develop the best, you know, we've got a new LP as you know, to strive to be at its best employer. I want to do that in a Mia. I want to make sure we can actually do that. We attract, we retain and we grow and we develop that. >>And the diversity has been a huge theme of this event. It's front and center in virtually every company. >>I am. I'm usually passionate about diversity. I'm proud actually that when I was back at Schneider, I launched something called the power women network. We're a network of a hundred senior women and we meet every month. I've also got a podcast out there. So if anyone's listening, it's called power. Women's speak. It is, I've done 16 over the pandemic with CEOs of women podcast, our women speak >>Or women speak oh, >>And Spotify and >>Everything else. >>And, um, you know, what I love about what we're doing is AWS on diversity and you heard Adam onstage, uh, talk to this. We've got our restock program where we really help under employed and unemployed to get a 12 week intensive course and get trained up on thought skills. And the other thing is, get it helping young girls, 12 to 15, get into stem. So lots of different things on the whole, but we need to do a lot more of course, on diversity. And I look forward to helping our clients through that as well. >>Well, we had, we had the training VP on yesterday. It's all free trainings free. >>We've got such a digital skills issue that I love that we've said 29 million people around the world, free cloud training. >>Literally the th the, the gap there between earnings with cloud certification, you can be making six figures like with cloud training. So, I mean, it's really easy. It's free. It's like, it's such a great thing. >>Have you seen the YouTube video on Charlotte Wilkins? Donald's fast food. She changed her mind. She wanted to take Korea. She now has a tech career as a result of being part of restock. Awesome. >>Oh, really appreciate. You got a lot of energy and love, love the podcast. I'm subscribing. I'm going to listen. We love doing the podcast as well. So thanks for coming on the >>Queue. Thank you so much for having me >>Good luck on anemia and your plans. Thank you. Okay. Cube. You're watching the cube, the leader in global tech coverage. We go to the events and extract the signal from the noise. I'm John furrier with Dave, a lot to here at re-invent physical event in person hybrid event as well. Thanks for watching.

>>from around the globe. It's the Cube with coverage of Coop con and cloud, native con Europe 2020 Virtual brought to you by Red Hat, The Cloud Native Computing Foundation and its ecosystem partners >>Welcome back. This is the Cube coverage of Cube Con Cloud, native con, the European show for 2020. I'm your host to Minuteman. And when we talk about the container world, we talk about what's happening in cloud. Native storage has been one of those sticking points. One of those things that you know has been challenging, that we've been looking to mature and really happy to welcome back to the program two of our cube alumni to give us the update on the state of storage for the container world. Both of them are oh, founders and CEOs. First of all, we have Xiang Yang from Rancher Labs, of course, was recently acquired by Sue Save it and the intention to acquire on and also joining us from early the relay. Who is with port works? Shang Amerli. Thanks so much for joining us. Thank you. Thank you. Alright. So early. I actually I'm going to start with you just cause you know we've seen, you know, a couple of waves of companies working on storage. In this environment, we know storage is difficult. Um, And when we change how we're building things, there's architectural things that can happen. Eso maybe if you could just give us a snapshot, you know, Port works, you know, was created to help unpack this. You know, straight on here in 2020 you know, where you see things in the overall kind of computer storage landscape? >>Absolutely. Still, before I kind of jump into port works. I just want to take a minute to publicly congratulate the the whole rancher team, and and Shang and Shannon And will China have known those folks for a while there? They're kind of true entrepreneurs. They represent the serial entrepreneur spirit that that so many folks know in the valley, and so, you know, great outcome for them. We're very happy for them and ah, big congrats and shout out to the whole team. What works is is a little over five years old, and we've been kind of right from the inception of the company recognized that to put containers in production, you're gonna have to solve, not just the orchestration problem. But the issue of storage and data orchestration and so in a natural kubernetes orchestrates containers and what works orchestrates storage and data. And more specifically, by doing that, what we enable is enterprises to be able to take APS that are containerized into production at scale and and have high availability. Disaster recovery, backup all of the things that for decades I t has had to do and has done to support application, reliability and availability. But essentially we're doing it for purpose with the purpose build solution for containerized workloads. >>Alright, shaming. Of course, storage is a piece of the overall puzzle that that ranchers trying to help with. Maybe if you could just refresh our audience on Longhorn, which your organization has its open source. It's now being managed by the CN. CF is my understanding. So help us bring Longhorn into the discussion >>thanks to. So I'm really glad to be here. We've I think rancher and port work started about the same time, and we started with a slightly different focus. More is exactly right to get containers going, you really need both so that the computer angle orchestrating containers as well as orchestrating the storage and the data. So rancher started with, ah, it's slightly stronger focus on orchestrating containers themselves, but pretty quickly, we realized, as adoption of containers grow, we really need it to be able to handle ah, storage feather. And like any new technology, you know, uh, Kubernetes and containers created some interesting new requirements and opportunities, and at the time, really, they weren't. Ah, a lot of good technologies available, you know, technologies like rook and SEF at the time was very, very premature, I think, Ah, the You know, we actually early on try to incorporate ah, the cluster technology. And it was just it was just not easy. And And at the time I think port Works was, ah, very busy developing. Ah, what turned out to be there flagship product, which we end up, end up, uh, partnering very, very closely. But but early on, we really had no choice but to start developing our own storage technology. So Long horn. As a piece of container storage technology, it's actually almost as oh, there's rancher itself. When about funding engineers, we hired he he ended up, you know, working on it and Then over the years, you know the focus shift that I think the original version was written in C plus plus, and over the years it's now being completely re written in Golan. It was originally written more for Docker workload. Now, of course, everything is kubernetes centric. And last year we you know, we we decided to donate the Longhorn Open Source project to CN CF. And now it's a CN CF sandbox project, and the adoption is just growing really quickly. And just earlier this year, we we finally ah decided to we're ready to offer a commercial support for it. So So that's that's where rancher is. And with longhorn and container storage technology. >>Yeah, it has been really interesting to watch in this ecosystem. A couple of years ago, one of the Q con shows I was talking to people coming out of the Believe It was the Sigs, the special interest group for storage, and it was just like, Wow, it was heated. Words were, you know, back and forth. There's not a lot of agreement there. Anybody that knows the storage industry knows that you know standards in various ways of doing things often are contentious and there's there's differences of opinion. Look at the storage industry. You know, there's a reason why there's so many different solutions out there. So maybe it love to hear from early. From your standpoint, things are coming to get a little bit more. There are still a number of options out there. So you know, why is this kind of coop petition? I actually good for the industry? >>Yeah, I think this is a classic example of Coop petition. Right? Let's let's start with the cooperation part right? The first part of time the you know, the early days of CN, CF, and even sort of the Google Communities team, I think, was really very focused on compute and and subsequent years. In the last 34 years, there's been a greater attention to making the whole stack works, because that's what it's going to take to take a the enterprise class production and put it in, you know, enterprise class application and put it in production. So extensions like C and I for networking and CS I container storage interface. We're kind of put together by a working group and and ah ah you know both both in the CN CF, but also within the kubernetes Google community. That's you talked about six storage as an example. And, you know, as always happens, right? Like it It looks a little bit in the early days. Like like a polo game, right where folks are really? Ah, you know, seemingly, uh, you know, working with each other on on top of the pool. But underneath they're kicking each other furiously. But that was a long time back, and we've graduated from then into really cooperating. And I think it's something we should all be proud of. Where now the CS I interface is really a A really very, very strong and complete solution tow, allowing communities to orchestrate storage and data. So it's really strengthened both communities and the kubernetes ecosystem. Now the competition part. Let's kind of spend. I want to spend a couple of minutes on that too, right? Um, you know, one of the classic things that people sometimes confuse is the difference between an overlay and an interface. CSC is wonderful because it defines how the two layers off essentially kind of old style storage. You know, whether it's a san or ah cloud, elastic storage bucket or all of those interact with community. So the the definition of that interface kind of lay down some rules and parameters for how that interaction should happen. However, you still always need an overlay like Port Works that that actually drives that interface and enables Kubernetes to actually manage that storage. And that's where the competition is. And, you know, she mentioned stuff and bluster and rook and kind of derivatives of those. And I think those have been around really venerable and and really excellent products for born in a different era for a different time open stack, object storage and all of that not really meant for kind of primary workloads. And they've been they've been trying to be adapted for, for for us, for this kind of workload. Port Works is really a built from right from the inception to be designed for communities and for kubernetes workloads at enterprise scale. And so I think, you know, as I as I look at the landscape, we welcome the fact that there are so many more people acknowledging that there is a vital need for data orchestration on kubernetes right, that that's why everybody and their brother now has a CS I interface. However, I think there's a big difference between having an interface. This is actually having the software that provides the functionality for H. A, D R. And and for backup, as as the kind of life cycle matures and doing it not just at scale, but in a way that allows kind of really significant removal or reduction off the storage admin role and replaces it with self service that is fully automated within communities. Yeah, if I >>can, you know, add something that that I completely agree. I mean, over the Longhorns been around for a long time. Like I said, I'm really happy that over the years it hasn't really impacted our wonderful collaborative partnership with what works. I mean, Poll works has always been one of our premier partners. We have a lot of, ah, common customers in this fight. I know these guys rave about what works. I don't think they'll ever get out for works. Ah, home or not? Uh huh. Exactly. Like Morissette, you know, in the in the storage space, there's interface, which a lot of different implementations can plugging, and that's kind of how rancher works. So we always tell people Rancher works with three types of storage implementations. One is let we call legacy storage. You know, your netapp, your DMC, your pure storage and those are really solid. But they were not suddenly not designed to work with containers to start with, but it doesn't matter. They've all written CS I interfaces that would enable containers to take advantage of. The second type is some of the cloud a block storage or file storage services like EBS, GFS, Google Cloud storage and support for these storage back and the CS I drivers practically come with kubernetes itself, so those are very well supported. But there's still a huge amount of opportunities for the third type of you know, we call container Native Storage. So that is where Port Works and the Longhorn and other solutions like open EBS storage OS. All these guys fitting is a very vibrant ecosystem of innovation going on there. So those solutions are able to create basically reliable storage from scratch. You know, when you from from just local disks and they're actually also able to add a lot of value on top of whatever traditional or cloud based, persistent storage you already have. So so the whole system, the whole ecosystem, is developing very quickly. A lot of these solutions work with each other, and I think to me it's really less of a competition or even Coop petition. It's really more off raising the bar for for the capabilities so that we can accelerate the amount of workload that's been moved onto this wonderful kubernetes platform in the end of the benefit. Everyone, >>Well, I appreciate you both laying out some of the options, you know, showing just a quick follow up on that. I think back if you want. 15 years ago was often okay. I'm using my GMC for my block. I'm using my netapp for the file. I'm wondering in the cloud native space, if we expect that you might have multiple different data engine types in there you mentioned you know, I might want port works for my high performance. You said open EBS, very popular in the last CN CF survey might be another one there. So is do we think some of it is just kind of repeating itself that storage is not monolithic and in a micro service architecture. You know, different environments need different storage requirements. >>Yeah, I mean quick. I love to hear more is view as well, especially about you know, about how the ecosystem is developing. But from my perspective, just just the range of capabilities that's now we expect out of storage vendors or data management vendors is just increased tremendously. You know, in the old days, if you can store blocks to object store file, that's it. Right. So now it's this is just table stakes. Then then what comes after that? There will be 345 additional layers of requirements come all the way from backup, restore the our search indexing analytics. So I really think all of this potentially off or in the in the bucket of the storage ecosystem, and I just can't wait to see how this stuff will play out. I think we're still very, very early stages, and and there, you know what? What, what what containers did is they made fundamentally the workload portable, but the data itself still holds a lot of gravity. And then just so much work to do to leverage the fundamental work load portability. Marry that with some form of universal data management or data portability. I think that would really, uh, at least the industry to the next level. Marie? >>Yeah. Shanghai Bean couldn't. Couldn't have said it better. Right? Let me let me let me kind of give you Ah, sample. Right. We're at about 160 plus customers now, you know, adding several by the month. Um, just with just with rancher alone, right, we are. We have common customers in all common video expedient Roche March X, Western Asset Management. You know, charter communications. So we're in production with a number off rancher customers. What are these customers want? And why are they kind of looking at a a a Port works class of solution to use, You know, Xiang's example of the multiple types, right? Many times, people can get started with something in the early days, which has a CS I interface with maybe say, $10 or 8 to 10 nodes with a solution that allows them to at least kind of verify that they can run the stack up and down with, say, you know, a a rancher type orchestrator, workloads that are containerized on and a network plug in and a storage plugging. But really, once they start to get beyond 20 notes or so, then there are problems that are very, very unique to containers and kubernetes that pop up that you don't see in a in a non containerized environment, right? Some. What are some of these things, right? Simple examples are how can you actually run 10 to hundreds of containers on a server, with each one of those containers belonging to a different application and having different requirements? How do you actually scale? Not to 16 nodes, which is sort of make typically, maybe Max of what a San might go to. But hundreds and thousands of notes, like many of our customers, are doing like T Mobile Comcast. They're running this thing at 600 thousands of notes or scale is one issue. Here is a critical critical difference that that something that's designed for Kubernetes does right. We are providing all off the storage functions that Shang just described at container granted, granularity versus machine granularity. One way to think about this is the old Data center was in machine based construct. Construct everything you know. VM Ware is the leader, sort of in that all of the way. You think of storage as villains. You think of compute and CPUs, everything. Sub sub nets, right? All off. Traditional infrastructure is very, very machine centric. What kubernetes and containers do is move it into becoming an app defined control plane, right? One of the things were super excited about is the fact that Kubernetes is really not just a container orchestrator, but actually a orchestrator for infrastructure in an app defined way. And by doing that, they have turned, uh, you know, control off the infrastructure via communities over to a kubernetes segment. The same person who uses rancher uses port works at NVIDIA, for example to manage storage as they use it, to manage the compute and to manage containers. And and that's marvellous, because now what has happened is this thing is now fully automated at scale and and actually can run without the intervention off a storage admin. No more trouble tickets, right? No more requests to say, Hey, give me another 20 terabytes. All of that happens automatically with the solution like port works. And in fact, if you think about it in the world of real time services that we're all headed towards right Services like uber now are expected in enterprises machine learning. Ai all of these things analytics that that change talk about are things that you expect to run in a fully automated way across vast amounts of data that are distributed sometimes in the edge. And you can't do that unless you're fully automated and and not really the storage admin intervention. And that's kind of the solution that we provide. >>Alright, well, we're just about out of time. If I could just last piece is, you know, early and saying to talk about where we are with long for and what we should expect to see through the rest of this year and get some early for you to you know, what differentiates port works from Just, you know, the open source version. So And maybe if we start with just kind of long or in general and then really from from your standpoint, >>yeah, so it's so so the go along one is really to lower the bar for folks to run state for workloads on on kubernetes we want you know, the the Longhorn is 100% open source and it's owned by CN cf now. So we in terms of features and functionalities is obviously a small subset of what a true enterprise grade solution like Port Works or, um, CEO on that that could provide. So there's just, you know, the storage role. Ah, future settle. The roadmap is very rich. I don't think it's not really Ranchers go Oh, our Longhorns goal to, you know, to try to turn itself into a into a plug in replacement for these enterprise, great storage or data management solutions. But But they're you know, there's some critical critical feature gaps that we need address. And that's what the team is gonna be focusing on, perhaps for the rest of the year. >>Yeah, uh, still, I would I would kind of, you know, echo what Chang said, right? I think folks make it started with solutions, like longer or even a plug in connector plug in with one of their existing storage vendors, whether it's pure netapp or or EMC from our viewpoint, that's wonderful, because that allows them to kind of graduate to where they're considering storage and data as part of the stack. They really should that's the way they're going to succeed by by looking at it as a whole and really with, You know, it's a great way to get started on a proof of concept architecture where your focus initially is very much on the orchestration and the container ization part. But But, as Xiang pointed out, you know what what rancher did, what I entered it for Kubernetes was build a simple, elegant, robust solution that kind of democratized communities. We're doing the same thing for communities storage right? What Port works does is have a solution that is simple, elegant, fully automated, scalable and robust. But more importantly, it's a complete data platform, right? We we go where all these solutions start, but don't kind of venture forward. We are a full, complete lifecycle management for data across that whole life cycle. So there's many many customers now are buying port works and then adding deal right up front, and then a few months later they might come back and I'd backup from ports. So two shanks point right because of the uniqueness of the kubernetes workload, because it is an app defined control plane, not machine to find what is happening is it's disrupting, Just like just like virtualization day. VM exist today because because they focused on a VM version off. You know, the their backup solution. So the same thing is happening. Kubernetes workloads are district causing disruption of the D r and backup and storage market with solutions like sports. >>Wonderful. Merlin Chang. Thank you so much for the updates. Absolutely. The promise of containers A Z you were saying? Really, is that that Atomic unit getting closer to the application really requires storage to be a full and useful solution. So great to see the progress that's being made. Thank you so much for joining us. >>Welcome, Shannon. We look forward to ah, working with you as you reach for the stars. Congratulations again. We look >>forward to the containing partnership morally and thank you. Still for the opportunity here. >>Absolutely great talking to both of you And stay tuned. Lots more coverage of the Cube Cube Con cloud, native con 2020 Europe. I'm stew minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah, yeah

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to theCUBE, I'm Lisa Martin with John Furrier. We are covering our second day of our event Big Data SV. We've had some great conversations, John, yesterday, today as well. Really looking at Big Data, digital transformation, Big Data, plus data science, lots of opportunity. We're excited to welcome back to theCUBE an alumni, Satyen Sangani, the co-founder and CEO of Alation. Welcome back! >> Thank you, it's wonderful to be here again. >> So you guys finish up your fiscal year end of December 2017, where in the first quarter of 2018. You guys had some really strong results, really strong momentum. >> Yeah. >> Tell us what's going on at Alation, how are you pulling this momentum through 2018. >> Well, I think we have had an enterprise focused business historically, because we solve a very complicated problem for very big enterprises, and so, in the last quarter we added customers like American Express, PepsiCo, Roche. And with huge expansions from our existing customers, some of whom, over the course of a year, I think went 12 X from an initial base. And so, we found some just incredible momentum in Q4 and for us that was a phenomenal cap to a great year. >> What about the platform you guys are doing? Can you just take a minute to explain what Alation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. >> Yeah. >> What's the update? Take a minute, talk about the update. >> Absolutely, so, you certainly know, John, but Alation's a data catalog and a data catalog essentially, you can think of it as Yelp or Amazon for data and information side of the enterprise. So if you think about how many different databases there are, how many different reports there are, how many different BI tools there are, how many different APIs there are, how many different algorithms there are, it's pretty dizzying for the average analyst. It's pretty dizzying for the average CIO. It's pretty dizzying for the average chief data officer. And particularly, inside of Fortune 500s where you have hundreds of thousands of databases. You have a situation where people just have too much signal or too much noise, not enough signal. And so what we do is we provide this Yelp for that information. You can come to Alation as a catalog. You can do a search on revenue 2017. You'll get all of the reports, all of the dashboards, all of the tables, all of the people that you might need to be able to find. And that gives you a single place of reference, so you can understand what you've got and what can answer your questions. >> What's interesting is, first of all, I love data. We're data driven, we're geeks on data. But when I start talking to folks that are outside the geek community or nerd community, you say data and they go, "Oh," because they cringe and they say, "Facebook." They see that data issues there. GDPR, data nightmare, where's the store, you got to manage it. And then, people are actually using data, so they're realizing how hard (laughs) it is. >> Yeah >> How much data do we have? So it's kind of like a tropic disillusionment, if you will. Now they got to get their hands on it. They've got to put it to work. >> Yeah. >> And they know that So, it's now becoming really hard (laughs) in their mind. This is business people. >> Yeah. >> They have data everywhere. How do you guys talk to that customer? Because, if you don't have quality data, if you don't have data you can trust, if you don't have the right people, it's hard to get it going. >> Yeah. >> How do you guys solve that problem and how do you talk to customers? >> So we talk a lot about data literacy. There is a lot of data in this world and that data is just emblematic of all of the stuff that's going on in this world. There's lots of systems, there's lots of complexity and the data, basically, just is about that complexity. Whether it's weblogs, or sensors, or the like. And so, you can either run away from that data, and say, "Look, I'm going to not, "I'm going to bury my head in the sand. "I'm going to be a business. "I'm just going to forget about that data stuff." And that's certainly a way to go. >> John: Yeah. >> It's a way to go away. >> Not a good outlook. >> I was going to say, is that a way of going out of business? >> Or, you can basically train, it's a human resources problem fundamentally. You've got to train your people to understand how to use data, to become data literate. And that's what our software is all about. That's what we're all about as a company. And so, we have a pretty high bar for what we think we do as a business and we're this far into that. Which is, we think we're training people to use data better. How do you learn to think scientifically? How do you go use data to make better decisions? How do you build a data driven culture? Those are the sorts of problems that I'm excited to work on. >> Alright, now take me through how you guys play out in an engagement with the customer. So okay, that's cool, you guys can come in, we're getting data literate, we understand we need to use data. Where are you guys winning? Where are you guys seeing some visibility, both in terms of the traction of the usage of the product, the use cases? Where is it kind of coming together for you guys? >> Yeah, so we literally, we have a mantra. I think any early stage company basically wins because they can focus on doing a couple of things really well. And for us, we basically do three things. We allow people to find data. We allow people to understand the data that they find. And we allow them to trust the data that they see. And so if I have a question, the first place I start is, typically, Google. I'll go there and I'll try to find whatever it is that I'm looking for. Maybe I'm looking for a Mediterranean restaurant on 1st Street in San Jose. If I'm going to go do that, I'm going to do that search and I'm going to find the thing that I'm looking for, and then I'm going to figure out, out of the possible options, which one do I want to go to. And then I'll figure out whether or not the one that has seven ratings is the one that I trust more than the one that has two. Well, data is no different. You're going to have to find the data sets. And inside of companies, there could be 20 different reports and there could be 20 different people who have information, and so you're going to trust those people through having context and understanding. >> So, trust, people, collaboration. You mentioned some big brands that you guys added towards the end of calendar 2017. How do you facilitate these conversations with maybe the chief data officer. As we know, in large enterprises, there's still a lot of ownership over data silos. >> Satyen: Yep. >> What is that conversation like, as you say on your website, "The first data catalog designed for collaboration"? How do you help these organizations as large as Coca-Cola understand where all the data are and enable the human resources to extract values, and find it, understand it, and trust it? >> Yeah, so we have a very simple hypothesis, which is, look, people fundamentally have questions. They're fundamentally curious. So, what you need to do as a chief data officer, as a chief information officer, is really figure out how to unlock that curiosity. Start with the most popular data sets. Start with the most popular systems. Start with the business people who have the most curiosity and the most demand for information. And oh, by the way, we can measure that. Which is the magical thing that we do. So we can come in and say, "Look, "we look at the logs inside of your systems to know "which people are using which data sets, "which sources are most popular, which areas are hot." Just like a social network might do. And so, just like you can say, "Okay, these are the trending restaurants." We can say, "These are the trending data sets." And that curiosity allows people to know, what data should I document first? What data should I make available first? What data do I improve the data quality over first? What data do I govern first? And so, in a world where you've got tons of signal, tons of systems, it's totally dizzying to figure out where you should start. But what we do is, we go these chief data officers and say, "Look, we can give you a tool and a catalyst so "that you know where to go, "what questions to answer, who to serve first." And you can use that to expand to other groups in the company. >> And this is interesting, a lot of people you mentioned social networks, use data to optimize for something, and in the case of Facebook, they they use my data to target ads for me. You're using data to actually say, "This is how people are using the data." So you're using data for data. (laughs) >> That's right. >> So you're saying-- >> Satyen: We're measuring how you can use data. >> And that's interesting because, I hear a lot of stories like, we bought a tool, we never used it. >> Yep. >> Or people didn't like the UI, just kind of falls on the side. You're looking at it and saying, "Let's get it out there and let's see who's using the data." And then, are you doubling down? What happens? Do I get a little star, do I get a reputation point, am I being flagged to HR as a power user? How are you guys treating that gamification in this way? It's interesting, I mean, what happens? Do I become like-- >> Yeah, so it's funny because, when you think about search, how do you figure out that something's good? So what Google did is, they came along and they've said, "We've got PageRank." What we're going to do is we're going to say, "The pages that are the best pages are the ones "that people link to most often." Well, we can do the same thing for data. The data sources that are the most useful ones are the people that are used most often. Now on top of that, you can say, "We're going to have experts put ratings," which we do. And you can say people can contribute knowledge and reviews of how this data set can be used. And people can contribute queries and reports on top of those data sets. And all of that gives you this really rich graph, this rich social graph, so that now when I look at something it doesn't look like Greek. It looks like, "Oh, well I know Lisa used this data set, "and then John used it "and so at least it must answer some questions "that are really intelligent about the media business "or about the software business. "And so that can be really useful for me "if I have no clue as to what I'm looking at." >> So the problem that you-- >> It's on how you demystify it through the social connections. >> So the problem that you solve, if what I hear you correctly, is that you make it easy to get the data. So there's some ease of use piece of it, >> Yep. >> cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. >> Satyen: That's right. >> So this seems to be the challenge. So, if I'm a customer and I have a problem, the profile of your target customer or who your customers are, people who need to expand and operationalize data, how would you talk about it? >> Yeah, so it's really interesting. We talk about, one of our customers called us, sort of, the social network for nerds inside of an enterprise. And I think for me that's a compliment. (John laughing) But what I took from that, and when I explained the business of Alation, we start with those individuals who are data literate. The data scientists, the data engineers, the data stewards, the chief data officer. But those people have the knowledge and the context to then explain data to other people inside of that same institution. So in the same way that Facebook started with Harvard, and then went to the rest of the Ivies, and then went to the rest of the top 20 schools, and then ultimately to mom, and dad, and grandma, and grandpa. We're doing the exact same thing with data. We start with the folks that are data literate, we expand from there to a broader audience of people that don't necessarily have data in their titles, but have curiosity and questions. >> I like that on the curiosity side. You spent some time up at Strata Data. I'm curious, what are some of the things you're hearing from customers, maybe partners? Everyone used to talk about Hadoop, it was this big thing. And then there was a creation of data lakes, and swampiness, and all these things that are sort of becoming more complex in an organization. And with the rise of myriad data sources, the velocity, the volume, how do you help an enterprise understand and be able to catalog data from so many different sources? Is it that same principle that you just talked about in terms of, let's start with the lowest hanging fruit, start making the impact there and then grow it as we can? Or is an enterprise needs to be competitive and move really, really quickly? I guess, what's the process? >> How do you start? >> Right. >> What do people do? >> Yes! >> So it's interesting, what we find is multiple ways of starting with multiple different types of customers. And so, we have some customers that say, "Look, we've got a big, we've got Teradata, "and we've got some Hadoop, "and we've got some stuff on Amazon, "and we want to connect it all." And those customers do get started, and they start with hundreds of users, in some case, they start with thousands of users day one, and they just go Big Bang. And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that. We have other customers that say, "Look, we're going to start with a team of 10 people "and we're going to see how it grows from there." And, we can accommodate either model or either approach. From our prospective, you just have to have the resources and the investment corresponding to what you're trying to do. If you're going to say, "Look, we're going to have, two dollars of budget, and we're not going to have the human resources, and the stewardship resources behind it." It's going to be hard to do the Big Bang. But if you're going to put the appropriate resources up behind it, you can do a lot of good. >> So, you can really facilitate the whole go big or go home approach, as as well as the let's start small think fast approach. >> That's right, and we always, actually ironically, recommend the latter. >> Let's start small, think fast, yeah. >> Because everybody's got a bigger appetite than they do the ability to execute. And what's great about the tool, and what I tell our customers and our employees all day long is, there's only metric I track. So year over year, for our business, we basically grow in accounts by net of churn by 55%. Year over year, and that's actually up from the prior year. And so from my perspective-- >> And what does that mean? >> So what that means is, the same customer gave us 55 cents more on the dollar than they did the prior year. Now that's best in class for most software businesses that I've heard. But what matters to me is not so much that growth rate in and of itself. What it means to me is this, that nobody's come along and says, "I've mastered my data. "I understand all of the information side of my company. "Every person knows everything there is to know." That's never been said. So if we're solving a problem where customers are saying, "Look, we get, and we can find, and understand, "and trust data, and we can do that better last year "than we did this year, and we can do it even more "with more people," we're going to be successful. >> What I like about what you're doing is, you're bringing an element of operationalizing data for literacy and for usage. But you're really bringing this notion of a humanizing element to it. Where you see it in security, you see it in emerging ecosystems. Where there's a community of data people who know how hard it is and was, and it seems to be getting easier. But the tsunami of new data coming in, IOT data, whatever, and new regulators like GDPR. These are all more surface area problems. But there's a community coming together. How have you guys seen your product create community? Have you seen any data on that, 'cause it sounds like, as people get networked together, the natural outcome of that is possibly usage you attract. But is there a community vibe that you're seeing? Is there an internal collaboration where they sit, they're having meet ups, they're having lunches. There's a social aspect in a human aspect. >> No, it's humanal, no, it's amazing. So in really subtle but really, really powerful ways. So one thing that we do for every single data source or every single report that we document, we just put who are the top users of this particular thing. So really subtly, day one, you're like, "I want to go find a report. "I don't even know "where to go inside of this really mysterious system". Postulation, you're able to say, "Well, I don't know where to go, but at least I can go call up John or Lisa," and say, "Hey, what is it that we know about this particular thing?" And I didn't have to know them. I just had to know that they had this report and they had this intelligence. So by just discovering people in who they are, you pick up on what people can know. >> So people of the new Google results, so you mentioned Google PageRank, which is web pages and relevance. You're taking a much more people approach to relevance. >> Satyen: That's right. >> To the data itself. >> That's right, and that builds community in very, very clear ways, because people have curiosity. Other people are in the mechanism why in which they satisfy that curiosity. And so that community builds automatically. >> They pay it forward, they know who to ask help for. >> That's right. >> Interesting. >> That's right. >> Last question, Satyen. The tag line, first data catalog designed for collaboration, is there a customer that comes to mind to you as really one that articulates that point exactly? Where Alation has come in and really kicked open the door, in terms of facilitating collaboration. >> Oh, absolutely. I was literally, this morning talking to one of our customers, Munich Reinsurance, largest reinsurance customer or company in the world. Their chief data officer said, "Look, three years ago, "we started with 10 people working on data. "Today, we've got hundreds. "Our aspiration is to get to thousands." We have three things that we do. One is, we actually discover insights. It's actually the smallest part of what we do. The second thing that we do is, we enable people to use data. And the third thing that we do is, drive a data driven culture. And for us, it's all about scaling knowledge, to centers in China, to centers in North America, to centers in Australia. And they've been doing that at scale. And they go to each of their people and they say, "Are you a data black belt, are you a data novice?" It's kind of like skiing. Are you blue diamond or a black diamond. >> Always ski in pairs (laughs) >> That's right. >> And they do ski in pairs. And what they end up ultimately doing is saying, "Look, we're going to train all of our workforce to become better, so that in three, 10 years, we're recognized as one of the most innovative insurance companies in the world." Three years ago, that was not the case. >> Process improvement at a whole other level. My final question for you is, for the folks watching or the folks that are going to watch this video, that could be a potential customer of yours, what are they feeling? If I'm the customer, what smoke signals am I seeing that say, I need to call Alation? What are some of the things that you've found that would tell a potential customer that they should be talkin' to you guys? >> Look, I think that they've got to throw out the old playbook. And this was a point that was made by some folks at a conference that I was at earlier this week. But they basically were saying, "Look, the DLNA's PlayBook was all about providing the right answer." Forget about that. Just allow people to ask the right questions. And if you let people's curiosity guide them, people are industrious, and ambitious, and innovative enough to go figure out what they need to go do. But if you see this as a world of control, where I'm going to just figure out what people should know and tell them what they're going to go know. that's going to be a pretty, a poor career to go choose because data's all about, sort of, freedom and innovation and understanding. And we're trying to push that along. >> Satyen, thanks so much for stopping by >> Thank you. >> and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. >> I appreciate the time too. >> Thank you. >> And thanks John! >> And thank you. >> Thanks for co-hosting with me. For John Furrier, I'm Lisa Martin, you're watching theCUBE live from our second day of coverage of our event Big Data SV. Stick around, we'll be right back with our next guest after a short break. 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>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.